Epigenetic Age Prediction with Sequence-Conditioned Co-Methylation Graph Networks

Epigenetic clocks estimate biological age from DNA methylation profiles and are widely used in ageing research. Graph-based clocks model relationships among CpG sites but typically omit the local DNA sequence surrounding each site, despite its potential influence on methylation. Using sequence context requires careful interpretation because the sequence of each CpG site is fixed across individuals: improved prediction may reflect informative local context or merely provide a way to distinguish CpG sites. We introduce a sequence-conditioned graph clock that jointly models sample-specific methylation, local sequence context, and relationships among CpG sites, with sequence conditioning accounting for only $0.05\%$ of the model's parameters. To determine what drives any improvement, we use matched comparisons that distinguish the contributions of sequence context and CpG relationships while testing whether gains depend on the correct sequence–site correspondence. Across 37 blood methylation studies, sequence conditioning consistently improves biological-age prediction, reducing mean test MAE from $3.550$ to $3.405$ years. This improvement largely disappears when sequences are assigned to the wrong CpG sites, showing that correct local context, rather than CpG-site identity alone, accounts for most of the gain. Prediction averaging further reduces MAE to $3.128$ years, and the model performs favourably in an exploratory application to ovarian cancer samples. These findings show that local DNA sequence provides useful information for co-methylation graph clocks and highlight the importance of distinguishing sequence information from CpG-site identity.